With the increasing popularity of the internet it is increasingly common for users to view media content such as video on distributed networks. For example, network video can be viewed on the web through services such as YouTube (YouTube is a Trademark of Google, Inc.). However, with increasing quantities of video content becoming available on networks users find it difficult to enjoy a network video in its entirety due to time constraints. It is therefore desirable to effectively and accurately locate most interesting parts of a video, that is portions of a video that are interesting to many people, allowing increased accessibility to the video content within the time constraints of many users.
In practice, interesting portions of video can be identified manually and video can be manually edited, presented or arranged for users in advance. With the continuous growth of the amount of video however, making editing and arranging video in this way requires significant manual effort and nonetheless suffers from the significant disadvantage that a determination of which portions are “interesting” is subjective, on the personal preferences and interests of the editor or arranger.
Automated editing technology also exists that can be used to reduce the overall quantity of video, such as by compaction or other reduction. Such technology implemented using computer video processing techniques may provide users with shorter versions of video content for consumption in reduced period of time, retaining key portions of an original video. In general there are two kinds of such automated editing technology: dynamic video summary which involves temporarily arranging some key scenes; and static video summary which is a representation generated by the key frames within some important scenes. However, using either of these techniques it is necessary to process video metadata using a video analysis method, leading to a large amount of computing which nonetheless may fail to reflect the interests of the ultimate audience.